COMPMID-1017: Implement dilated convolution in NEON, OpenCL, and GC
Change-Id: If4626ec9e215e14dffe22e80812da5bac84a52e2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125734
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index dee1608..348722c 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -45,12 +45,13 @@
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
- bool has_bias, bool is_fully_connected, bool is_flatten)
+ bool has_bias, bool is_fully_connected, bool is_flatten, const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
+ ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
if(is_flatten) /* Called by FlattenLayer */
{
@@ -59,7 +60,7 @@
}
else if(!is_fully_connected) /* Called by ConvolutionLayer */
{
- std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_dims.width, kernel_dims.height, conv_info);
+ std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_dims.width, kernel_dims.height, conv_info, dilation);
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(2) * kernel_dims.area() + (has_bias ? 1 : 0)));
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != (out_dims.first * out_dims.second));
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(2) != 1);
@@ -91,11 +92,13 @@
int input_stride_y,
int input_stride_z,
int fixed_point_position,
- int pad_value)
+ int pad_value,
+ int dilation_x,
+ int dilation_y)
{
const int kernel_size2 = kernel_width * kernel_height;
- const int x_e = top_left_x + kernel_width;
- const int y_e = top_left_y + kernel_height;
+ const int x_e = top_left_x + kernel_width * dilation_x;
+ const int y_e = top_left_y + kernel_height * dilation_y;
// Linearize volume
int d = 0;
@@ -104,12 +107,12 @@
// 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs
for(; d <= (kernel_depth - 3); d += 3)
{
- for(int y = top_left_y; y < y_e; ++y)
+ for(int y = top_left_y; y < y_e; y += dilation_y)
{
if((y < 0 || y >= input_h) && has_pads)
{
// All the values will be the offset (will be zeros when not quantized)
- for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+ for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr)
{
*(out_ptr + 0 * kernel_size2) = pad_value;
*(out_ptr + 1 * kernel_size2) = pad_value;
@@ -118,7 +121,7 @@
}
else
{
- for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+ for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr)
{
if((x < 0 || x >= input_w) && has_pads)
{
@@ -141,7 +144,7 @@
// Left over
for(; d < kernel_depth; d++)
{
- for(int y = top_left_y; y < y_e; ++y)
+ for(int y = top_left_y; y < y_e; y += dilation_y)
{
if((y < 0 || y >= input_h) && has_pads)
{
@@ -151,7 +154,7 @@
}
else
{
- for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+ for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr)
{
if((x < 0 || x >= input_w) && has_pads)
{
@@ -251,7 +254,9 @@
input_stride_y,
input_stride_z,
_input->info()->fixed_point_position(),
- offset);
+ offset,
+ _dilation.x(),
+ _dilation.y());
},
in, out);
}
@@ -309,27 +314,28 @@
}
NEIm2ColKernel::NEIm2ColKernel()
- : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false)
+ : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false), _dilation(1U, 1U)
{
}
void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
- bool has_bias, bool is_fully_connected, bool is_flatten)
+ bool has_bias, bool is_fully_connected, bool is_flatten, const Size2D &dilation)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Perform validation step
ARM_COMPUTE_UNUSED(is_fully_connected, is_flatten);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten, dilation));
_input = input;
_output = output;
_conv_info = conv_info;
_kernel_width = kernel_dims.width;
- _kernel_height = kernel_dims.height,
+ _kernel_height = kernel_dims.height;
+ _dilation = dilation;
_convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
_kernel_width, _kernel_height,
- _conv_info);
+ _conv_info, _dilation);
_has_bias = has_bias;
unsigned int stride_x = 0;
@@ -340,7 +346,8 @@
&& (std::equal(input->info()->tensor_shape().cbegin() + 3,
input->info()->tensor_shape().cend(),
output->info()->tensor_shape().cbegin() + 1))
- && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
+ && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding())
+ && ((dilation.x() == 1) && (dilation.y() == 1));
Window window = calculate_max_window(*input->info(), Steps());
@@ -407,9 +414,9 @@
}
Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
- bool has_bias, bool is_fully_connected, bool is_flatten)
+ bool has_bias, bool is_fully_connected, bool is_flatten, const Size2D &dilation)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten, dilation));
return Status{};
}